{"product_id":"ml-scalability-handling-big-data-with-efficiency-scale-ml-models-for-large-datasets-and-high-performance-tasks-9798264687761","title":"ML Scalability Handling Big Data with Efficiency: Scale ML models for large datasets and high-performance tasks","description":"\u003cp\u003e • Author(s): Isandro Myles\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Programming - Algorithms\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eUnlock the power of scalable machine learning to handle big data.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eIn \u003cb\u003eML Scalability\u003c\/b\u003e, you'll learn how to \u003cb\u003escale machine learning models\u003c\/b\u003e to efficiently handle \u003cb\u003elarge datasets\u003c\/b\u003e and high-performance tasks. This practical guide will show you how to optimize your machine learning workflows, build scalable solutions, and apply advanced techniques to tackle complex problems that require massive amounts of data.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside, you'll discover how to: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eUnderstand ML scalability\u003c\/b\u003e: Learn why scalability is crucial in modern machine learning and how it impacts performance, data processing, and model deployment.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eScale machine learning models for \u003cb\u003ebig data\u003c\/b\u003e using \u003cb\u003edistributed frameworks\u003c\/b\u003e like \u003cb\u003eApache Spark\u003c\/b\u003e, \u003cb\u003eDask\u003c\/b\u003e, and \u003cb\u003eHadoop\u003c\/b\u003e for parallel processing.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eBuild \u003cb\u003eefficient pipelines\u003c\/b\u003e that process and clean massive datasets using \u003cb\u003epandas\u003c\/b\u003e, \u003cb\u003ePySpark\u003c\/b\u003e, and \u003cb\u003eTensorFlow Data API\u003c\/b\u003e.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eImplement \u003cb\u003edistributed training\u003c\/b\u003e strategies with \u003cb\u003emulti-GPU\/TPU setups\u003c\/b\u003e and \u003cb\u003edata parallelism\u003c\/b\u003e for faster model training.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eOptimize \u003cb\u003edata storage\u003c\/b\u003e and access patterns for large datasets with \u003cb\u003eHDF5\u003c\/b\u003e, \u003cb\u003eParquet\u003c\/b\u003e, and \u003cb\u003eApache Arrow\u003c\/b\u003e to streamline workflows.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eUse \u003cb\u003ecloud platforms\u003c\/b\u003e like \u003cb\u003eAWS\u003c\/b\u003e, \u003cb\u003eGoogle Cloud\u003c\/b\u003e, and \u003cb\u003eAzure ML\u003c\/b\u003e to scale models and integrate with other big data tools.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eLearn \u003cb\u003emodel performance optimization\u003c\/b\u003e techniques such as \u003cb\u003ebatch processing\u003c\/b\u003e, \u003cb\u003emini-batch gradient descent\u003c\/b\u003e, and \u003cb\u003edistributed learning\u003c\/b\u003e.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eApply \u003cb\u003escalable algorithms\u003c\/b\u003e for tasks such as \u003cb\u003eregression\u003c\/b\u003e, \u003cb\u003eclassification\u003c\/b\u003e, and \u003cb\u003eclustering\u003c\/b\u003e that work efficiently at scale.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eImplement \u003cb\u003emodel serving\u003c\/b\u003e and deployment strategies using \u003cb\u003eTensorFlow Serving\u003c\/b\u003e, \u003cb\u003eKubeFlow\u003c\/b\u003e, and \u003cb\u003eMLflow\u003c\/b\u003e for scalable production environments.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eUse \u003cb\u003ehyperparameter tuning\u003c\/b\u003e and \u003cb\u003eautomated machine learning\u003c\/b\u003e (AutoML) techniques to further optimize model performance in large-scale settings.\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003ePacked with \u003cb\u003estep-by-step tutorials\u003c\/b\u003e, \u003cb\u003ereal-world examples\u003c\/b\u003e, and \u003cb\u003ebest practices\u003c\/b\u003e, this book empowers you to tackle big data challenges and scale your machine learning models to handle massive datasets efficiently.\u003c\/p\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eData scientists\u003c\/b\u003e and \u003cb\u003emachine learning engineers\u003c\/b\u003e seeking to optimize and scale their models for large datasets\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eCloud architects\u003c\/b\u003e and \u003cb\u003eengineers\u003c\/b\u003e looking to leverage cloud infrastructure for scalable ML solutions\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eResearchers\u003c\/b\u003e and \u003cb\u003estudents\u003c\/b\u003e focused on scaling machine learning for high-performance tasks\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eDevelopers\u003c\/b\u003e working with big data and looking to optimize machine learning workflows\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e\u003cb\u003eBusiness professionals\u003c\/b\u003e looking to apply scalable ML solutions to handle large-scale data problems\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eMaster the techniques to scale your machine learning models and process big data efficiently for high-performance results.\u003c\/b\u003e\u003c\/p\u003e","brand":"Atlantic 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